Artificial Intelligence

Our example realizations
Automotive company - We have successfully accomplished this task for the automotive company, that is a leader in the Internet channel.

What does your marketing really cost?
Not all of your clients will respond positively to the campaign they are targeting. They will be more or less convinced of your offer. With the help of machine learning methods, for each recipient, we will define a score as a propensity to accept the proposal, so that you will bear the cost of the campaign only for those customers who are likely to buy your product. This way you will reduce costs and increase ROI.

Our example realizations
We have successfully built such models in the telco and HR industry.

Maximize your sales team's financial performance
In managing the sales team, intuition and experience are extremely important. However, to improve the efficiency of sales people, you can also involve the data that you have on board. Dealer's experience, region, number of activities performed, historical sales data and many other factors make up a model that helps to understand how much every of these parameters affects the sales value of a given dealer. By creating relevant reports, analyzes and models, we have helped the Internet company increase sales at a rate of 30-60% per annum for several years.

Establish sales performance goals for the organization
Forecasting demand and sales is an important element that allows you to control your business not only during the annual budgeting. By analyzing internal data (transaction volumes, seasonality, employment status) and external (macroeconomic indicators, currency rates, weather, competition activities), we build artificial intelligence models that allow forecasting results with satisfactory accuracy. We also have experience in building algorithms that correct forecasts every day, so that the management will keep the information on whether everything is going according to plan or whether corrective decisions should be taken.

Detect customers who are likely to cancel a subscription to your service
One of the biggest problems of service companies is the phenomenon of customers resignations. When someone decides to change the mobile operator, it is usually too late. Our intelligent anti-churn models identify dissatisfied customers a few months before an irreversible decision to refer them to a special "rescue" path. We have 20 years of experience in this field.

Process data analytics to predict failures
Sudden and unforeseen breakdowns, e.g. in heavy industry, gas & oil industry or in transport are a big problem and cost. Companies are trying to predict that a given failure is approaching, e.g. due to material fatigue. As in the case of churn, our algorithms analyze the historical data provided by the client (but not only this data) and are very likely to determine the possibility and time of failure. In this way, the customer can avoid it, e.g. by exchanging the given component.

Clever organizations develop fraud detection techniques
Offensive sales strategies are focused on acquiring a large number of customers. This means that some of them will turn out to be unfair. However, using artificial intelligence algorithms based on the customer's behavioral history, we can indicate transactions suspected of being a phishing scam. The anti-fraud system that monitors the events and transactions data stream prevents unlimited financial losses.

Analyze of creditworthiness evaluate and make recommendation on loan proposals
We have experience in building credit scoring for loan companies. Algorithms assessing the legitimacy of the application acceptance help to reduce the number of bad payers and increase sales to those for whom the conditions for obtaining credit without intelligent scoring would be too restrictive.

Help businesses narrow the pool and reach the people that they want to be talking to
To optimize the costs of customer service and increase the level of its satisfaction, companies use customer segmentation algorithms, differentiating relationship management depending on the segment. We create segmentation using machine learning algorithms "without a teacher" to select natural segments or use algorithms "with a teacher" to optimize segmentation for a given KPI (e.g. cross-selling value or customer satisfaction level)